A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning
In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Ran...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/15/3268 |
| _version_ | 1797586366052171776 |
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| author | Wangfang Xu Wenjia Rao Longbao Wei Qianqian Wang |
| author_facet | Wangfang Xu Wenjia Rao Longbao Wei Qianqian Wang |
| author_sort | Wangfang Xu |
| collection | DOAJ |
| description | In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models. |
| first_indexed | 2024-03-11T00:22:15Z |
| format | Article |
| id | doaj.art-2de14cc1819641d8860fc10e85a4f82e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:22:15Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2de14cc1819641d8860fc10e85a4f82e2023-11-18T23:14:23ZengMDPI AGMathematics2227-73902023-07-011115326810.3390/math11153268A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine LearningWangfang Xu0Wenjia Rao1Longbao Wei2Qianqian Wang3China Academy for Rural Development, Zhejiang University, Hangzhou 310058, ChinaSchool of Science, Hangzhou Dianzi University, Hangzhou 310018, ChinaChina Academy for Rural Development, Zhejiang University, Hangzhou 310058, ChinaSchool of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaIn this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models.https://www.mdpi.com/2227-7390/11/15/3268global economic policy uncertainty indexprincipal component analysisrandom matrix theorycomplex networkGARCH-MIDAS modelfuture price volitility |
| spellingShingle | Wangfang Xu Wenjia Rao Longbao Wei Qianqian Wang A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning Mathematics global economic policy uncertainty index principal component analysis random matrix theory complex network GARCH-MIDAS model future price volitility |
| title | A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning |
| title_full | A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning |
| title_fullStr | A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning |
| title_full_unstemmed | A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning |
| title_short | A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning |
| title_sort | normalized global economic policy uncertainty index from unsupervised machine learning |
| topic | global economic policy uncertainty index principal component analysis random matrix theory complex network GARCH-MIDAS model future price volitility |
| url | https://www.mdpi.com/2227-7390/11/15/3268 |
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